process analytical technology concepts and principles.pdf

7/28/2019 Process analytical technology Concepts and Principles.pdf

1/854 Pharmaceutical Technology OCTOBER 2003 www.pharmtech.com

he current process analytical technology (PAT) initiativeunderway within FDA exemplifies the latest consortiumbetween FDA and the industry that aims to encourage

the concepts of quality by design,use of computerizeddata gathering and evaluation techniques,and process- andproduct-monitoring methods through advanced instrumenta-tion and data evaluation.Although this partnership betweenFDA and the industry is relatively new (2001),methods relatedto PAT such as chemometrics have been studied and have beenin use for quite some time.Yet,the PAT initiative has raised sev-eral questions:What does PAT really encompass? Is it a newtechnology or is it a series of proven scientific principles? Howcan PAT be used in a pharmaceutical operation to gain betterprocess understanding and possibly reduce cycle times and as-sociated costs?

This article discusses the concepts that embody PAT. Em-phasis is placed on chemometrics,which is the use of mathe-matical and statistical models to extract and interpret chemi-cal data.

What is PAT?

FDA defines PAT as a system for the analysis and control of manufacturing

processes based on timely measurements of critical qualityparameters and performance attributes of raw materials andin-process materials

a process to ensure acceptable end-product quality at the com-

pletion of the processing (1).FDA also states that PAT involves the optimal application ofprocess analytical chemistry (PAC)

tools feedback process-control strategies information management tools and/or productprocess op-

timization strategies for the manufacture of pharmaceuticals(1).

In summary,PAT can be defined as the optimal application ofPAC tools, feedback process-control strategies,informationmanagement tools,and/or productprocess optimization strate-gies to the manufacture ofpharmaceuticals.

PAT focuses on the principles of building quality into theproduct and process as well as continuous process improve-ment.A few examples of PAT tools and strategies are as follows:

Process AnalyticalTechnologyConcepts and PrinciplesMark L.Balboni

M ark L. Balboni is a senior compliance

consultant at KMI, a division of PAREXEL

International, LLC, 28241 Crown Valley

Parkway, F601, Laguna Niguel, CA 92677,

tel/fax 949.830.2355, [email protected].

T

Process analytical technologies are a

multifaceted group of concepts that

comprise one of many initiatives that form

FDAs Pharmaceutical GMPs for the 21st

Century.Yet what constitutes a process

analytical technology or whether it is in fact

a new technology remains unclear.This

article outlines the key concepts that define

process analytical technology and

emphasizes the relevant theory and

applications of chemometrics.

PHOTODISC,

INC.



at-line, in-line,or on-line measurement of process qualityand performance attributes using a variety of instrumenta-tion and measurement strategies such as near-infrared (NIR),vibrational, acoustical, and X-ray spectroscopy

chemometric approaches such as multivariate statistical andpattern recognition methods.

real-time data and information management systems forprocess control (2).FDAs PAT initiative is supported by many large pharmaceuti-cal corporations and distinguished members of academia.

Another potential advantage of PAT is the opportunity toplace more reliance on in-process testing as the basis for finalproduct release.This type of product-release methodology re-quires both a significant amount of data to be compiled andheavy correlations to be determined by analysis and evaluation(3). The theory is that product release could be based on rela-tionships (i.e.,correlations between observed in-process test re-sults and predictive qualitative results of the final product) es-

tablished during productprocess development and confirmedby both validation and routine review of productprocess datafor commercial lots.These relationships coupled with confir-mation testing of the finished product would serve as the basisfor release,or the product could possibly be released on thebasis of the observed in-process results and how a currentlyproduced lot favorably compares with other previously releasedproduct lots.

FDAs PAT initiative is being spearheaded by the Center forDrug Evaluation and Research (CDER),Office of Pharmaceu-tical Science.Since 2001,FDA has held and participated in a se-ries of PAT meetings as part of the Process Analytical Tech-

nologies Subcommittee for the Office of Pharmaceutical Sciencesand with the FDA Science Board.These meetings have includednot only FDA personnel but also pharmaceutical industry rep-resentatives,members of academia,and engineering and con-sulting professionals.

The PAT initiative is part of a larger FDA initiative calledPharmaceutical CGMPs for the 21st Century:A Risk-BasedApproach(4). The agency seeks to improve the regulation ofpharmaceutical manufacturing using a science- and risk-basedapproach to product-quality regulation while incorporating anintegrated quality-systems approach.

Complete descriptions of FDAs PAT initiative and the Phar-

maceutical CGMPs for the 21st Centuryapproach,includingelectronic links to associated documents and reference materi-als,are provided in References 1 and 4.

PACand in/at/on-line monitoringAccording to Hailey et al., PAC is the technique of gatheringanalytical information in real time at the point ofmanufacture(5). As they noted, PAC places an emphasis on the processrather than the final product,including an understanding ofthe relationship between final product specification [sic] andthe critical variables during the manufacturing process.

Although the approaches and instrumentation currently

being discussed are in some cases categorized as being novel ornew, real-time measurement (PAC) has existed for some time(e.g.,real-time temperature monitoring of reaction vessels dur-

ing the synthesis of active pharmaceutical ingredients). Oneparticular industryuniversity partnership,The Center forProcess Analytical Chemistry (CPAC) at the University ofWash-ington,has been in existence since at least 1986 (6).

Although PAC is not a new approach,many of the relatedtechniques have been tested and used only on a limited basis

by a very small percentage of the pharmaceutical industry.Thefollowing is a partial list ofthe various sensors and instrumen-tation recently discussed at the 2003 Arden House Conferenceeither in-use or currently being evaluated for feasible use forproduction monitoring: NIR spectroscopy for moisture determination X-ray spectroscopy radio frequency for moisture determination microwaves for moisture determination RAMAN spectroscopy,with vibrational spectroscopy being

the most common.RAMAN complements IR spectroscopyand is used for raw-material identification,polymorph dif-

ferentiation,and reaction monitoring. fluorescence for water quality on-line measurement of color X-ray fluorescence for the detection of inorganic materials photoacoustic spectroscopy.

Because most of these technologies are extremely sophisti-cated,one must realize that the key emphasis of PAT is not somuch how to collect the data or what kind of instrumentationshould be used,but rather what data should be collected,whatis done with these data,and what associated conclusions arereached.Therefore,a complete and thorough understanding ofthe manufacturing process is paramount.

Chemometrics

To fully understand PAT,one must first understand the sciencebehind manufacturing processes,including how these processesoperate, their limitations,and their expected outcomes.

In 1974 Sante Wold,from the Institute of Chemistry at UmeaUniversity (Umea,Sweden) described the art of extractingchemically relevant information from data provided in chem-ical experimentsaschemometrics(7).He stated that this art isheavily dependent on the use of different kinds of mathemat-ical modelsand that it was important to have knowledge instatistics,numerical analysis,and applied mathematics,including

the challenge to structure the chemical problem to a form thatcan be expressed in a mathematical relation.Twenty years later,during a 1994 presentation,Professor Wold

said his definition of chemometrics had not changed much.Hesaid chemometrics requires us to ask ourselves How do we getchemically relevant information out of measured chemical data;how do we represent and display this information;and,how dowe get such information into data?

In 1996,Wise and Gallagher stated chemometrics is the sci-ence of relating measurements made on a chemical system tothe state of the system via application of mathematical or sta-tistical methods(8).Similarly,Hardy noted that data are raw

information,both qualitative and quantitative(9).He observedthat in and of themselves,raw data are meaninglessand thata method ofanalysis and a modelare needed to gain knowl-



edge from the data.He also identified the clear need for aprocess to convert data to knowledge.

Dosage forms recently have been described as complexmultifactorial physiochemical systems(10), sometimes re-ferred to asmultivariate. Multivariate analysis consists ofmethods of statistical analysis of multivariate data,charac-

terized as consisting of several observations on each set of ob-jects or mathematically represented by a collection of pointsin a finite-dimensional Euclidean spaceRP (11). So, multi-variate analysis is an important statistical method widely usedby chemometricians.

Described below are a few of the tools commonly used bychemometricians,all of which are helpful for evaluating pro-cessing data generated during pharmaceutical unit operationsor when synthesizing active pharmaceutical ingredients.

Principle component analysis (PCA).PCA is a technique used toexpress variations of many variables by a small number of in-dicies (11).Wise and Gallagher describe PCA as a favorite tool

of chemometricians for data compression and information ex-traction and note that PCA finds combinations ofvariables orfactors that describe major trends in a data set(8). Sans et al.(12) observed that PCA can be used to determine the numberof components that influence the data of the process as well asto identify the chemical meaning of the components.They pro-posed that PCA enables one to approach multivariate chemi-cal problems in order to obtain underlying information aboutthe correlation of the raw data and the real meaning interpre-tation.Finally,Wise and Gallagher (8) acknowledged that gen-erally there is a great deal of correlated or redundant informa-tion in process measurements,and often essential information

lies not in any individual process variable but how the variableschange with respect to one anotheror how they co-vary.Theyalso noted that some sort of signal averagingwould be use-ful in cases in which a large amount of noise is created from thevolume of data and a lack of clear understanding of the dataexists.

However,Wu et al. point out two limitations of PCA: When an object is added or removed as displayed in a plane,

principal components (PCs) must be recalculated all overagain by following the process of selection and interpretationof the PCs.

No more than two PCs at a time can be viewed (inspected)

in a plane,and this prevents one from using the informationcontained in other PCs (13).The authors also note that their star-plotmethod could be

used as an alternative way to display and analyze multivariatedata.

Application of PCA to chemical processes.Wise and Gallagher (8)studied data obtained from a slurry-fed ceramic reactor usingthermocouples placed at 20 locations (8). They found a greatdeal of correlation as the data generated followed a sawtoothpattern. In addition, the study revealed the following: PCA performed on this data found three factors that captured

approximately 97% of the variance in the data set.

This previously noted finding allowed 16 variables to be re-placed with three new ones,which were linear combinationsof the original variables.

The sawtooth pattern was attributed to changes in the levelof molten glass,which was a controlled variable.

The three factors (PCs) were identified as the level of moltenglass in the reactor,the variation between two groups ofmea-sured locations,and the variation of overall process temper-ature (also a controlled variable).

Sans et al.also used PCA to determine stoichiometric mod-els from on-line spectroscopy for selecting the number of re-actions and the number of chemical absorbing species to bet-ter describe a chemical reaction (12). They chose to usespectroscopy methods because they can reveal informationabout the dynamic evolution of the reaction mass during achemical reaction. Although the outcome of this study is notdiscussed in this article,Sans et al. did note that semibatchprocesses are examples of complex reaction networks that gen-erally are difficult to interpret because of the large number ofreactions occurring simultaneously and because of the effectsrelated to the addition of materials that may cause complex vol-

ume changes during the processing time.Multiway principle component analysis (MPCA).An analog ofPCAis what is known asmultiway PCA,which is equivalent to per-forming PCA on a very large two-dimensional matrix formed byunfolding the three-way arrayX into one of six possible ways,only three ofwhich are mathematically unique(8).General PCAmethods do not take into account the ordered nature of the datasets,meaning that the data were not collected in a sequentialmanner.Multiway methods take into account the ordered (se-quential) nature regarding when data were generated becausethe data usually are organized into time-ordered blocks that areeach representative of a single sample or process run(8).

Nomikos and MacGregor (14) describe another aspect ofMPCA; namely,the use of on-line measurements to identifysignificant deviations from the normal operating behavior byusing SPC charts.An empirical model is based on data gener-ated when the process is operating within a state of control.

They noted,future unusual events are detected by referencingthe measured process behavior against the in-controlmodeland its statistical properties.

Partial least squares (PLS).Wise and Gallagher described PLSas a regression related to both principle component regression(regression of properties on the principle component scores ofthe measured variable) and multiple linear regression (also

known as inverse least squares)(8). They observed that thisanalysis can be used to predict properties of a system based onvariables that are only indirectly related to the property.Ac-cordingly,by using PLS,one attempts to find factorsthat bothcapture variation and achieve correlation.

Multiway partial least squares (MPLS). Nomikos and MacGre-gor observed that MPCA using statistical process-control chartsonly makes use of the process variable trajectory measurements(X) taken throughout the duration of the batch(14). In con-trast,multiway partial least squares (MPLS) can be performedusing both the process data (X) and the product quality data(Y),and focuses more on the variance ofX that is more pre-

dictive for the product qualityY(14).Other statistical tools.Two other statistical tools that may be

useful in PAT efforts are



capability studies,which measure the ability of the process toconsistently meet specifications by evaluating select processoutputs and calculating the average and ranges over a speci-fied time on control charts.From these studies,capability in-dicies Cp (used to evaluate the variation) and Cpk (used toevaluate the centering of the process) are calculated.

design of experiments,which are experiments that involvechanging one or more of the process inputs and measuringthe results to one or more of the process outputs (15).

Rapid microbiology test methodsAnother part of FDAs PAT initiative involves discussion

about the feasibility of developing so-called rapidmicrobiol-ogy methods (emphasis added,as most microbiological test-ing can take anywhere from several days to a couple of weeksto complete). This topic is of heightened interest within theindustry as manufacturers seek additional ways to reduce cycletimes.

Rapid microbiology testing was discussed during the FDAAdvisory Committee for Pharmaceutical Science meeting on2324 October 2002 (16).During that meeting,the committeeidentified at least two problems or risks associated with the de-velopment of rapid microbiology methods;namely, validation of test methods may not yield results equal to those

for traditional test methods acceptance by regulators (e.g., FDA).

The group also categorized microbial determinations as fol-lows: qualitative methods (presence or absence of microbes;e.g.,

sterility testing) quantitative methods (enumeration of microorganisms pre-

sent;e.g.,microbial limits tests)

microbial identification.FDA did concede that rapid microbiology test methods arean important part of the PAT initiative but that at this time ithas not yet been discussed extensively (16). FDA also com-mented that the general guidance document on PAT would notspecify details about rapid microbiology methods but wouldrather cover them in a general sense to encourage their use.Fi-nally, the group felt that microbial identification probably hasthe most rapid method systems presently available,and quan-titative testing probably has the least detection systems.

Real-time data information and management systems

Another PAT tool being discussed is the use of real-time datainformation and management systems.Although a detailedoverview of this topic is not provided in this article, some ofthe possible concerns include possible 21CFRPart 11 implica-tions,adding complexity to already complex computer systemarchitecture,and the amount of data retention necessary,giventhat continuous monitoring will generate very large volumesof data.

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With FDAs recent withdrawal of Part 11 guidance documents,it would be advisable to first review the anticipated PAT guid-ance document for information concerning data informationand management issues and secondly review the 21CFR Part11 implementation portion of the Pharmaceutical CGMPs forthe 21st Century Initiative,which includes both a Notice of

Availability and a draft guidance on Part 11.The draft guidanceon Part 11 attempts to clarify the scope and applicability of theregulation, which ultimately may undergo revision to clarifythe scope and requirements.

Benefits and challenges:an industry perspectiveMost industry representatives that either were involved in dis-cussions regarding the feasibility of PAT or who have conductedsuccessful PAT efforts thus far have had many more positivethan negative comments regarding the advantages of adoptingPAT principles.Positive perceived benefits of PAT include decrease in cycle times

lower costs increased efficiency and batch-to-batch consistency process fingerprinting (signature) that would be useful for val-

idation,scale-up,and confirming acceptable handling ofchanges increased process understanding and a decrease in variabil-

ity, rejects,and lot failures possible continuous processing and the ability to adjust process

on the basis of real-time monitoring data.

Conversely,the most-common perceived or actual challengesinclude product-approval delays by inclusion of PAT methodologies

into relatively traditional drug development and validationactivities

lack of a written PAT guidance document from FDA

an increase in the amount of data being generated,includingnot only what one should do with the extra data but also pos-sible Part 11 implications

increased pressures to meet aggressive filing timelines,addedcosts to make changes,lack of senior management support,and resource constraints (Source:Reference 1 and 2003 AAPSArden House Conference.

Where we go fromhereAlthough no one can clearly predict how widely accepted PATwill become,it is clear that FDA does support innovation.Theagency is appealing to the industry to take a more active role in

understanding its manufacturing processes and is seeking quickand effective resolution of problems associated with good man-ufacturing practices that result in rejected batches,stability fail-ures, field alerts,and product recalls.

Currently there are many actions a company can take toprepare for what FDA is calling the GMPs for the 21st Cen-tury.By reading the contents provided on FDAs Web site re-garding PAT (http://www.fda.gov/cder/OPS/PAT.htm), which

Circle/eINFO 47



includes committee meeting minutes and dozens of presen-tations,one will see that many large pharmaceutical firms al-ready have made significant accomplishments through thesuccessful implementation of PAT principles. In spite of theindustrys dwindling profit margins, fewer new products inthe pipeline,and a more competitive marketplace,these com-

panies already have recognized the positive aspects of imple-menting PAT.The following are some active steps that a com-pany can take right now.

Develop a plan for the future. Wait for FDAs much anticipated guidance document about

PAT before making significant decisions regarding how yourcompany will handle certain PAT principles (e.g.,validationof new analytical methods for a unique in-process test).

Analyze existing product lines and determine which may ben-efit most from PAT.For example,the compounding and fill-ing ofa well-established product that is a true solution com-posed of 99.5% water is probably not the best candidate for

PAT. The manufacture of complex dosage forms such astableted products,which also includes in-process monitor-ing using process control charts,would probably be a betterchoice. Products with recurring quality problems are alsogood candidates because process deviations or exceptions aresometimes results of a less-than-complete understanding ofthe process rather than more-obvious causative factors.

Obtain/retain employees with education,training,and expe-

rience in disciplines necessary to be successful in PAT efforts.For example,FDA is seeking to hire persons experienced inin-process/chemical engineering,PAC, chemometrics, andindustrial pharmacy.For your companys efforts to be suc-cessful,similar expertise will be needed.

Gain executive managements support for PAT.Although it

may be easier to make the scientific case,management mustunderstand how PAT could reduce cycle times and costs andadd value.Make a business case for adopting PAT.Benchmark with industry and academic partners. Speak with

company representatives from firms that already are using PATprinciples.Many major universities also are heavily involved inthe PAT initiative and could provide guidance, including,butnot limited to,the following schools that have had representa-tives at several FDA committee meetings about PAT: MIT Pharmaceutical Manufacturing Initiative Purdue University,School of Pharmacy Center for Process Analytical Chemistry at the University of

Washington.Texas A&M Department of Chemistry and Chemical Engi-

neering.Gather information about the topic.A significant amount of in-

formation concerning PAT can be found on FDAs Web site (1).Additional new information continues to be added,and plentyof information already is available on many university and in-dustry association Web sites regarding topics such as PAT,PAC,

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PCA, and chemometrics. Internet keyword searches can yieldsignificant additional information.

Attend future PATworkshops,seminars,or symposia.Find outwhat FDA and others are saying first-hand. If the workshopsare sponsored by an industry association,you may need to at-tend in person to get the handouts and presentations because

all of that information may not be published on FDAs or theassociations Web site.Provide comments directly to FDA using the Internet. If you have

substantial comments regarding FDAs PAT initiative and youfeel it is information that others could benefit from,considerproviding electronic comments to FDA.The agency already hasestablished a Web-based feedback tool for this purpose (www.fda.gov/ohrms/dockets/).You will need to search for docketnumber 03N-0059.Alternatively,you could send questions orcomments regarding the PAT initiative by email [email protected].

Ensure quality is designed into your products and processes.

Confirm compliance with good manufacturing practices.Re-member, CGMP is the minimum standard according to 21CFR211.1(a).Most of the successful companies strive for andachieve substantial compliance with these regulations as wellas carry out best practices.Poorly developed manufactur-ing processes,untrained employees,or equipment that hasnot been properly qualified will hinder any PAT efforts.

Complete thorough product and process development work

to ensure processes are adequately defined.Include robustspecifications and critical process-control points.

Confirm that active drug substances are well characterized. Use validation efforts to demonstrate consistency and repro-

ducibility,not as a means to conduct range finding,processadjustments,or enhancements.These are development tasks,

not validation. Optimize processes and improve yields after successful vali-

dation,not during validation. Consider process or equipment changes carefully,especially

postapproval, because this usually will result in much unan-ticipated work and undoubtedly more development activi-ties and data.Complete risk or impact assessments on thebasis of data and not on opinions or theories.

Many years ago,Alexander Hamilton said,Experience teaches that men are often so much governedby what they are accustomed to see and practice,that thesimplest and most obvious improvements, in the most

ordinary occupations,are adopted with hesitation, re-luctance, and by slow gradations. Men would resistchanges,so long as even bare support could be ensuredby an adherence to ancient courses,and perhaps evenlonger.Sometimes changes do take time, so we should look to the

future potential of PAT to enhance our pharmaceutical processes

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Continued on page 254


8/8

and applaud FDA for encouraging the application of new tech-nologies and for their willingness to work with industry on thisimportant initiative.

AcknowledgmentThe author would like to acknowledge the following individu-

als for their assistance in the writing of this article:KMI seniorcompliance consultant Eric S.Weilage and PAREXEL seniorbiostatistician Chunzhang Wu,PhD.

References1. CDER, Office of Pharmaceutical Sciences,Process and Analytical

Technologies Initiative,http://www.fda.gov/cder/OPS/ PAT.htm.2. A. Hussain and J. Famulare,FDAs PAT Initiative and its Role in the

Proposed Drug Quality System for the 21st Century,presented at theAAPS Arden House Conference,27 January 2003.

3. T.Layloff,Process Analytical Technology (PAT) Subcommittee Re-port,presented at the ACPS meeting 21 October 2002.

4. FDA,Pharmaceutical CGMPs for the 21st Century:A Risk-Based Ap-proach,http://www.fda.gov/cder/gmp/index.htm.

5. P.Hailey et al.,Automated System for the On-line Monitoring ofPow-der Blending Processes Using Near-Infrared Spectroscopy Part I.Sys-tem Development and Control,J. Pharma. Biomed. Analysis14,551559 (1996).

6. D.Illman,CPAC:An IndustryUniversity Cooperative Research Cen-ter for Process Analytical Chemistry,TrAC: Trends in Analytical Chem-istry5(7),164172 (1986).

7. S.Wold,Chemometrics:What Do We Mean with It and What Do WeWant from It?presented at InCINC 94,Institute of Chemistry,UmeaUniversity (Umea,Sweden,1994).

8. B.Wise and N. Gallager,The Process Chemometrics Approach toProcess Monitoring and Fault Inspection,J.Proc.Ctrl.6(6),329348(1996).

9. J.Hardy,Special Topics:Chemometrics,lecture presentation asso-

ciated with 3150: 710 (University of Akron, 2000), available athttp://ull.chemistry.uakron.edu/chemometrics/introduction.10. FDA,Emerging Issues in Pharmaceutical Manufacturing:Process An-

alytical Technology,science board meeting presentation (16 Novem-ber 2001).

11. N.Sugakkai,Iwanami Sugaku Ziten,original publication by IwanamiShoten Publishers (Tokyo,Japan,1954), copyright by Nihon Sugakkai(Mathematics Society of Japan);English translation provided by theMassachusetts Institute of Technology (1977).

12. D.Sans,R. Nomen,and J. Sempere,Interactive Self-Modelling ofChemical Reaction System Using Multivariate Data Analysis,sup-plement toComput.Chem.Eng.21,S631S636 (1997).

13. W.Wu et al.,The Star-Plot:an Alternative Display Method for Multi-variate Data in the Analysis of Food and Drugs,J. Pharma.Biomed.Analysis17(6-7),10011013 (September 1998).

14. P.Nomikos and J.MacGregor,Multiway Partial Least Squares in Mon-itoring Batch Processes,Chemometrics and Intelligent Laboratory Sys-tems30,97108 (1995).

15. FDA Global Harmonization Task Force Study Group #3,draft ProcessValidation Guidance (1 June 1998).

16. FDA Advisory Committee for Pharmaceutical Science transcripts,23October 2002.PT

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